The management of complicated networks such as telecommunications networks or sophisticated computer networks is tremendously expensive. A substantial portion of this cost arises from incomplete, incorrect or ambiguous knowledge about a network. For example, a telecommunications network operator may not have an accurate record of how network switches are configured, leading to failed attempts to fix problems or provision new services. This lack of knowledge can in some instances be remedied by polling the networking equipment to determine its actual settings.
However, a more fundamental ambiguity arises at the physical level of network cable management. Network cables may be added, removed or moved by support personnel for a variety of reasons, often to solve urgent problems. However, it is very difficult to maintain an accurate record of exactly which cable is connected to what device port of what piece of equipment, since the cables may so easily be connected, disconnected, and reconnected.
Typically, network cable locations and connections are tracked manually, by, for example, putting printed tags on each cable, storing the tag-to-cable mappings in a database, and then attempting to manually keep the database up to date. In addition, physical inventories of network offices, in which the cables are identified, tagged and mapped, are themselves typically performed manually. In a large telecommunications or computer network system, it is an extremely expensive proposition to keep track of every cable, where it is, where it runs, and what device port on which piece of equipment it is plugged into. As a result, equipment inventory databases are notoriously inaccurate, and the negative results include, inter alia, loss of network capacity, increased service times and a much greater chance of disruptive service errors.
Another problem, seemingly unrelated to the network cable connectivity problem discussed above, involves automated manufacturing systems. Such systems typically involve the automated assembly of components, requiring the attachment of two components (at a time) in a predetermined way. Computer vision systems are often used in such environments to provide feedback from the manufacturing process to the controlling software, thereby allowing the components to be located precisely and correctly in three-dimensional space relative to one another. For example, in an automobile manufacturing facility, if a hood is to be attached to a car body, a computer vision system may be able to determine that the two edges are attached evenly and at the correct distance from one another. Unfortunately however, vision systems are extremely expensive and work only very locally within a limited field of view. In addition, the three-dimensional location information must be indirectly inferred from two-dimensional camera images.
Thus, to address both of the above-described problems, it would be highly advantageous if there were an automated mechanism for tracking the precise three-dimensional physical locations of components, from which one could thereby determine the appropriate connectivity or alignment between them. For example, it would be highly desirable to be able to track the physical location of network cables in general, and to be able to identify the connections between cables and equipment device ports in particular. In addition, it would be highly desirable to be able to automatically determine the correct alignment between two components in an automated manufacturing system in a direct manner in a relatively inexpensive way.